{"uid":"cap_ZDmltOOsDFmQckSpeH54Y","slug":"warehouse-bins-string-normalisation-date-by-category-pivot-77fbe273","name":"Warehouse Bins — String Normalisation + Date by Category Pivot","description":"Warehouse bins — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied warehouse bins records (occupied_volume, available_volume). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-retail-13-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","occupied_volume","available_volume"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"store_group":{"type":"string","default":"","maxLength":80},"occupied_volume":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"available_volume":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","store_group":"group-a","occupied_volume":10,"available_volume":1},{"date":"2026-10-02","row_id":"row-2","store_group":"group-b","occupied_volume":20,"available_volume":2},{"date":"2026-10-03","row_id":"row-3","store_group":"group-a","occupied_volume":15,"available_volume":3},{"date":"2026-10-04","row_id":"row-4","store_group":"group-b","occupied_volume":30,"available_volume":4}],"maxItems":200,"minItems":1},"field":{"enum":["occupied_volume","available_volume"],"type":"string","default":"occupied_volume"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_D1kXWGSjWh2TOO1JCdFBF","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs two local analytical reports on warehouse bin records: string normalisation of text fields and a date-by-category pivot for occupied_volume or available_volume, across up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of warehouse bin records with store_group, date, occupied_volume, and available_volume columns — can you run string normalisation on the text fields and produce a date-by-category pivot for occupied_volume on this data?","exampleUseCases":[{"title":"Warehouse bin occupancy pivot by store","prompt":"I've got 150 rows of warehouse bin data with store groups like 'group-a' and 'group-b', dates ranging from October 2026, and occupied_volume figures — can you normalize the store group strings and pivot the occupied_volume by date and category so I can see trends per group?"},{"title":"Available volume cleanup and summary","prompt":"My warehouse dataset has some messy store group labels and I need a clean date-by-category breakdown of available_volume across all 80 rows — can you normalise the strings and run the pivot report on available_volume?"},{"title":"Retail bin utilisation reporting","prompt":"I have a week's worth of warehouse bin records for two retail store groups, about 56 rows total, with occupied and available volumes recorded daily — please normalise the text fields and give me a date-category pivot for occupied_volume so I can spot which group is filling up fastest."}],"resultDescription":"Two analytical reports: (1) a string-normalised version of the text fields (row_id, store_group) across the submitted rows, and (2) a date-by-category pivot table for the selected numeric field (occupied_volume or available_volume), grouped by store_group across dates — all computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, store_group) cause row rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Malformed JSON body causes a 400-level response"],"whenToPreferThis":"Choose this endpoint when you have warehouse bin records (with store_group, date, occupied_volume, available_volume) and need both string normalisation of categorical text fields and a date-by-category pivot in a single low-cost call. It is ideal for batch preprocessing of up to 200 rows offline with no need for live data lookups, predictions, or external integrations. Prefer it over general-purpose analytics tools when the data fits the warehouse bin schema exactly and cost predictability ($0.01 USDC for two reports) matters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T00:42:58.741Z","isFirstParty":false,"canonicalSlug":"warehouse-bins-string-normalisation-date-by-category-pivot-77fbe273"}